This is strange for me, because I have watched a lot of football in the last seven years. Like, thousands of matches.

Given all the other cool things there are to do in the world, I promise you I am not bragging when I say I have watched more football matches during that time than 99.9% of football fans, all because of my job. (To be fair, it is a pretty cool job.) I have learned a lot in that time, but I haven’t had that many paradigm shifts that I can recall. Michael Cox and Jonathan Wilson caused me to incorporate tactics into my thought process, but that’s about it.

All of these models evaluate shots taken by teams, but not by the player taking them or whether they are on target, provoke a save, or whatever. They simply evaluate them by looking at where the shots are taken from. After examining massive numbers of shots across leagues, we can say that the breakdown of shooting locations looks generally like this:

From Paul Riley’s SPAM model

The numbers in each box are how many shots it takes on average to score from those positions. Shots from central areas near the goal are very good. Shots from wide areas are pretty bad. Shots from beyond 25 yards are useless. This image has taken over my brain space to the point where I honestly don’t know how I thought about things like shooting before. There is only this now.

Once you know it, it seems so obvious. But I am dead certain that I didn’t know this last season… how odd.

The memory is hazy, but I’m pretty sure that I used to get excited seeing Steven Gerrard thump an open-play ball toward the goal from 30 yards. “Ooo, look how well he struck that!” A shot was a shot. Hooray for shooting!

Now when some fool blasts away from 30 yards, I kind of groan, and think, “Well, that was a waste of a perfectly good possession.” The world itself is still the same. My perception of the world has changed.

It’s not just watching football live that has changed for me, either. Shot location charts have completely modified how I evaluate players. Player statistics are still the baseline for everything I do regarding player analytics, but only a fool would assume they tell you the full story. It’s important to understand that player stats are also heavily constrained by tactical needs of the team (which is why Cesc Fabregas’s statistical profile at Barcelona looks completely different than his time at Arsenal), and also by the players surrounding them on the field.

Here’s an example from some profiles I wrote earlier this summer for Serie A players that is pretty illustrative:

According to a number of media that I follow, Ilicic is off to Leverkusen, presumably as a replacement for the departed Schurrle, while Leverkusen also keep a hefty profit in their pocket as well. The reporters don’t seem to rate him very well, but as I noted on Twitter, his stats are actually pretty good.

Year

Apps

G

A

ShpG

KP

Drib

Disp

Trn

PS%

NPG/90

Sot%

2013

29(2)

10

2

2.9

1.9

2

2

1.8

78.1

0.38

33

It bears noting that he played for Palermo this season, who were dreadful and got relegated. As a point of fact, he’s been at Palermo since he moved in 2010. The first season he was there, he put up an 8G/7A season in 30 starts, good stuff for a 23-year-old moving from Slovenia.

The goal rate here doesn’t crack the .4 threshold, but Ilicic’s key pass numbers are good enough to suggest he will become a better overall offensive performer when surrounded with better players. Instead of being a great big fish in a small pond, he has a skill set that should mesh well with the ongoing Leverkusen revolution.

The concern – and this is a fairly big one – is that his Shots on Target percentage is bad. You hope that this is a by-product of playing on a bad team and still trying to produce, but if it’s not, then Ilicic would likely perform better when shooting a bit less and setting up good teammates a bit more. Thankfully, we have the awesome shot heatmaps from Constantinos and Colin again.

Want to see what a heatmap looks like for a decent player who plays on a really bad team? Look no further.

Zero box penetration. [Insert your own joke here.]

Likely price: 10-12M euros

You know back when people with original iPhones were posting photos to Facebook and they were constantly fuzzy and irritating – like way worse than photos a normal camera would take? But because carrying around a separate camera and a smart phone is a pain in the ass, all we got were shitty, lens-covered-in-vaseline photos of friends and family for an entire year.

If you don’t remember it, trust me. It sucked. Looking at pictures online was suddenly migraine-inducing.

Then one day, someone got a new iPhone with a much better lens, and you let out a sigh of relief when you saw them, because you now understood that everyone’s social photos were not destined to suck for the rest of time. Progress had been made! But now, whenever you go back and look at photos from the relatively brief period of time, you scratch your head wondering why everyone’s photos look so awful.

Analytics is a bit like that.

In five years, no one will remember all of these things that we didn’t know about football. They will just assume people always knew this information. Except for Jamie Redknapp, who they will instead assume was on television for the sole purpose of making fans feel better about themselves.

Calls from the crowd to “Shoooooot!” from 30 yards will no longer be shouted by home fans at their own players, but instead will be used sarcastically toward opposing players to encourage them to give away possession for free. (I swear this used to happen to Emmanuel Eboue. And he used to listen.)

Analytics will change how fans watch the game.

It will change how fans and television analysts talk about the game.

It will change how everyone – including the guys managing the teams – think about the game.

And the best pieces of analysis will simply replace whatever knowledge might have been in its place before with new, corrected perspectives. Poof! You just got smarter.

Related

Interesting development, inputs and conclusions. The key word at StatsBomb seems to be constant development.

Giant strides taken in just a short time. Impressive!

I think I get everything you argue in your piece, but for one thing (hopefully). Now you may label me a certified moron, but I just need to ask.

The Ilicic example. Is it an illustration of the past that we should forget in the future world, i.e. a guy that wastes a lot of good potential opportunities by shooting in the red (chaff) zone. Or. Is it an illustration of a guy beating the SPAM model by scoring above the expected average outcome of the SPAM model?

//Peter

Heath Garlick

How many goals are scored as a result of shots taken in the wide and 25yrd+ areas, ie a shot is blocked and another player picks up possession in a better area and scores?

Differentgame

I haven’t got an exact figure but work around it suggests the answer is ‘not many’.

http://blogs.columbian.com/portland-timbers/ Chris Gluck

A very helpful article Ted that will go in my favs for future reference!

Raj

Can you elaborate on what the following mean? I’m pretty sure I know most of them but its always nice if you can elaborate on the abbreviations!
G
A
ShpG
KP
Drib
Disp
Trn
PS%
NPG/90
Sot%

Leron

It would be interesting to analyze a great long shot scorer like Steven Gerrard in his earlier (prime) years to see if this holds true for all players.

Toshack

Ted,
Just re-read your piece on Erik Lemela from MixedKnuts in June. So it seems Spurs picked up a real gem? And no CL team was interested enough. Strange…
//Peter